WO2020027513A1 - Système d'analyse d'image basé sur la syntaxe pour image compressée, et procédé de traitement d'interfonctionnement - Google Patents

Système d'analyse d'image basé sur la syntaxe pour image compressée, et procédé de traitement d'interfonctionnement Download PDF

Info

Publication number
WO2020027513A1
WO2020027513A1 PCT/KR2019/009374 KR2019009374W WO2020027513A1 WO 2020027513 A1 WO2020027513 A1 WO 2020027513A1 KR 2019009374 W KR2019009374 W KR 2019009374W WO 2020027513 A1 WO2020027513 A1 WO 2020027513A1
Authority
WO
WIPO (PCT)
Prior art keywords
image
moving object
analysis system
object region
unique
Prior art date
Application number
PCT/KR2019/009374
Other languages
English (en)
Korean (ko)
Inventor
이현우
정승훈
이성진
Original Assignee
이노뎁 주식회사
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 이노뎁 주식회사 filed Critical 이노뎁 주식회사
Publication of WO2020027513A1 publication Critical patent/WO2020027513A1/fr

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/136—Incoming video signal characteristics or properties
    • H04N19/137—Motion inside a coding unit, e.g. average field, frame or block difference
    • H04N19/139—Analysis of motion vectors, e.g. their magnitude, direction, variance or reliability
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/17—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object
    • H04N19/176—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/70—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by syntax aspects related to video coding, e.g. related to compression standards

Definitions

  • the present invention relates to a technique for improving the processing performance of a plurality of compressed images in a CCTV control system.
  • the present invention provides a syntax (eg, a motion vector, coding type) of a compressed image without the need for object identification and behavior recognition through complex image processing of the entire compressed image as in the conventional art in an image analysis system.
  • the present invention relates to a technique for improving the processing performance of a compressed image by extracting a region in which something meaningful movement exists in the image, that is, a moving object region, and analyzing the extracted result in conjunction with an image analysis system.
  • the reality is that the number of control personnel is very low compared to the installation status of CCTV cameras. In order to effectively perform video surveillance with such limited number of people, simply displaying CCTV images on the monitor screen is not enough. It is preferable to process the object to be effectively detected by detecting the movement of the object present in each CCTV image and displaying something in the corresponding area in real time. In this case, the monitoring personnel do not monitor the entire CCTV image with uniform interest, but monitor the CCTV image centering on the part where the object moves.
  • the video detection system adopts compressed video for the efficiency of the storage space.
  • high-compression complex video compression technologies such as H.264 AVC and H.265 HEVC have been adopted.
  • CCTV cameras generate and provide compressed images according to these technical specifications, and the video control system decodes the compressed images in reverse.
  • a process of image processing is required after decoding a compressed image to obtain a reproduced image, that is, a decompressed original image.
  • a video decoding apparatus includes a parser 11, an entropy decoder 12, an inverse converter 13, a motion vector operator 14, a predictor 15, and a deblocking filter ( 16) is configured to include.
  • These hardware modules process compressed video sequentially to decompress and restore the original video data.
  • the parser 11 parses the motion vector and the coding type for the coding unit of the compressed image.
  • Such a coding unit is generally an image block such as a macroblock or a subblock.
  • FIG. 2 is a flowchart illustrating a process of extracting a moving object by analyzing a compressed image and performing object classification and event identification through the conventional art.
  • the compressed image is decoded according to H.264 AVC and H.265 HEVC, etc. (S10), and the frame images of the reproduced image are downscaled to a small image, for example, 320 ⁇ 240 (S20).
  • the reason for downscaling resizing is to reduce processing burden in subsequent steps.
  • the moving objects are extracted through a complicated image analysis process, and the coordinates of the moving objects are calculated (S30).
  • object classification and event identification are performed using the extracted object thumbnail images and coordinates of the moving objects (S40).
  • An object of the present invention is to provide a technique for improving the processing performance of a plurality of compressed images in a CCTV control system.
  • an object of the present invention is to perform an image based on the syntax (eg, motion vector, coding type) of the compressed image without the need of object identification and behavior recognition through complex image processing of the entire compressed image in the image analysis system as in the prior art. It is to provide a technique for improving the processing performance of compressed images by extracting a region in which something meaningful movement exists, that is, a moving object region, and analyzing the extracted result in conjunction with an image analysis system.
  • the syntax eg, motion vector, coding type
  • a syntax-based image analysis system and an interworking processing method for a compressed image include a first step of parsing a bitstream of a compressed image to obtain a motion vector and a coding type for a coding unit.
  • the present invention is performed between the sixth step and the seventh step, the image analysis system sorts the thumbnail image and coordinate information on the basis of the unique ID and image analysis processing by the unit of unique ID to classify objects and events for the moving object area Performing identification; may be configured to further include.
  • the fifth step may include: determining whether a moving object region of the same object exists in a previous frame for each moving object region based on the calculation of the overlapping degree of the image blocks between the moving object regions; A 52nd step of determining whether a unique ID is pre-assigned for each moving object region according to the determination result of the 51st step; A fifty-third step of maintaining a pre-allocated Unique ID for the mobile object area in the Unique ID allocation state according to the determination result of the fifty-second step; A new step of allocating a unique ID to a mobile object area in the unique ID unassigned state according to the determination result of the step 52; If a unique ID has been assigned in the previous frame but disappeared from the current frame image is identified, step 55 for revoking the unique ID assigned to the disappeared mobile object region may be performed.
  • the present invention may further include a first step of identifying a plurality of adjacent image blocks (hereinafter, referred to as 'neighbor block') around a moving object area, which are performed between the fourth step and the fifth step; B) comparing a motion vector value with a second preset threshold value for a plurality of neighboring blocks; C) additionally marking a neighboring block having a motion vector value exceeding a second threshold as a moving object region; D) additionally marking a neighboring block having a coding type of an intra picture among the plurality of neighboring blocks as a moving object region;
  • the method may further include an e-step of performing interpolation on the plurality of moving object regions to additionally mark a predetermined number or less of unmarked image blocks surrounded by the moving object region as the moving object region.
  • the computer program according to the present invention is stored in the medium in combination with hardware to execute the syntax-based image analysis system and the interworking processing method for the compressed image as described above.
  • the present invention by quickly identifying the moving object region from the syntax of the compressed image without performing complicated image processing on the entire compressed image as in the prior art, only the identified portion is selectively processed in association with the image analysis system.
  • FIG. 1 is a block diagram showing a general configuration of a video decoding apparatus.
  • FIG. 2 is a flowchart illustrating a process of performing object classification and event identification by analyzing a compressed image in the prior art.
  • FIG. 3 is a view illustrating a concept in which an image analysis system and an object region identification device interoperate in the present invention.
  • FIG. 4 is a flowchart illustrating a process of interworking with an image analysis system based on compressed image syntax according to the present invention.
  • FIG. 5 is a flowchart illustrating an example of a process of detecting effective motion from a compressed image in the present invention.
  • FIG. 6 is a diagram illustrating an example of a result of applying an effective motion region detection process according to the present invention to a CCTV compressed image.
  • FIG. 7 is a flowchart illustrating an example of a process of detecting a boundary region for a moving object region in the present invention.
  • FIG. 8 is a diagram illustrating an example of a result of applying a boundary region detection process to the CCTV image of FIG. 6.
  • FIG. 9 is a diagram illustrating an example of a result of arranging a moving object region through interpolation with respect to the CCTV image of FIG. 8.
  • FIG. 10 is a diagram for one example in which a unique ID is assigned to a moving object area in the present invention.
  • FIG. 11 is a diagram for one example of deriving a thumbnail image for a moving object region in the present invention.
  • FIG. 12 is a diagram illustrating an example in which location information and size information are identified for a moving object region in the present invention.
  • FIG. 3 is a diagram illustrating a concept in which an image analysis system and an object region identification apparatus interoperate in the present invention.
  • the present invention is a technique for effectively processing a compressed image transmitted from a CCTV camera 100.
  • CCTV surveillance systems collect high-quality captured images from hundreds to tens of thousands of CCTV cameras 100 compressed with complex image compression algorithms (eg H.264 AVC, H.265 HEVC).
  • complex image compression algorithms eg H.264 AVC, H.265 HEVC.
  • the processing burden of the image analysis server is very high, and the maximum CCTV channel that one server can accommodate is usually only 16 channels.
  • the present invention increases the overall processing performance by linking the object region identification apparatus 200 to the general image analysis system 300.
  • the image analysis system 300 analyzes the contents of the image in the manner used in the related art, recognizes an object, and identifies an event (eg, an offender wandering, a wall story, a crime, a fight, etc.) according to a result of analyzing the behavior of the object.
  • an event eg, an offender wandering, a wall story, a crime, a fight, etc.
  • the image analysis system 300 in the present invention is not limited to operating according to the conventional image analysis technology, but may be so.
  • the object region identification apparatus 200 extracts a region in which something meaningful movement exists in the image, that is, a moving object region, based on the syntax of the compressed image (eg, motion vector, coding type), and then the thumbnail image or the like.
  • a structure that analyzes and processes location information in conjunction with the image analysis system 300 is adopted. Through this, the image analysis system 300 recognizes an object and analyzes the behavior of the object to significantly reduce the amount of data required to identify the event to improve the overall performance.
  • the object region identification apparatus 200 parses a bitstream of a compressed image without having to decode the compressed image, and obtains syntax information, for example, a motion vector, for each image block, that is, a macro block and a sub block.
  • the moving object region is quickly extracted through the motion vector and coding type information.
  • the moving object region thus obtained does not accurately reflect the boundary of the moving object, but its processing speed is about 20 times faster than image analysis, but it shows a certain level of reliability for the existence of significant movement.
  • the object area identification apparatus 200 quickly filters most of the compressed images based on the syntax, thereby reducing the burden on the image analysis system 300 and increasing the overall processing performance.
  • the moving object region extracted by the object region identification apparatus 200 is merely a lump of an image block estimated to include the moving object, there is a limit in determining something therefrom. This is because the contents of the images are not judged, but the characteristics of the movements in the images are distinguished. Accordingly, the moving object region identification information and the thumbnail image or position information of the moving object region acquired by the object region identification apparatus 200 are transmitted to the image analysis system 300.
  • the image analysis system 300 performs image analysis on thumbnail images derived from a series of frame images constituting the compressed image, for example, classifies objects by determining image contents in a moving object region and performs an event. To identify.
  • the object region identification apparatus 200 When the compressed image is transmitted as described above, the object region identification apparatus 200 quickly picks out image chunks that seem to be important because there is something moving, and the image analysis system 300 properly analyzes the contents of the selected image chunks to classify the objects. Eg people, cars, animals, etc.) and events.
  • the object area identification apparatus 200 receives an image analysis result, that is, an object classification result and an event identification result from the image analysis system 300, and provides the control personnel to utilize the image analysis result.
  • the object region identification apparatus 200 does not need to decode the compressed image in the process of extracting the moving object region.
  • the apparatus or software to which the present invention is applied should not perform the operation of decoding the compressed image, but the scope of the present invention is not limited.
  • an operation of decoding the compressed image partially or entirely may be performed.
  • the image analysis system 300 may be a system for performing a conventional image analysis process, but the scope of the present invention is not limited thereto.
  • FIG. 4 is a flowchart illustrating a process of interworking with an image analysis system based on compressed image syntax according to the present invention.
  • Step S100 An effective motion that can substantially recognize meaning is detected from the compressed image based on the motion vector of the compressed image, and the image region in which the effective motion is detected is set as the moving object region.
  • data of a compressed image is parsed according to video compression standards such as H.264 AVC and H.265 HEVC to obtain a motion vector and a coding type for a coding unit.
  • video compression standards such as H.264 AVC and H.265 HEVC to obtain a motion vector and a coding type for a coding unit.
  • the size of the coding unit is generally about 64x64 to 4x4 pixels and may be set to be flexible.
  • the motion vectors are accumulated for a predetermined time period (for example, 500 msec) for each image block, and it is checked whether the motion vector accumulation value exceeds a first predetermined threshold (for example, 20 pixels). If such an image block is found, it is considered that effective motion has been found in the image block and marked as a moving object area. On the other hand, even if a motion vector is generated, if the cumulative value for a predetermined time does not exceed the first threshold, the image change is assumed to be insignificant and ignored.
  • a predetermined time period for example, 500 msec
  • a first predetermined threshold for example, 20 pixels
  • Step S200 Detects how far the boundary region is to the moving object region detected in S100 based on the motion vector and the coding type. If a motion vector occurs above a second threshold (for example, 0) or a coding type is an intra picture by inspecting a plurality of adjacent image blocks centered on the image block marked as a moving object area, the corresponding image block is also moved. Mark as an object area. Through this process, the corresponding image block is substantially in the form of forming a lump with the moving object region detected in S100.
  • a second threshold for example, 0
  • a coding type is an intra picture by inspecting a plurality of adjacent image blocks centered on the image block marked as a moving object area, the corresponding image block is also moved. Mark as an object area.
  • an effective motion is found and there is a certain amount of motion in the vicinity of the moving object area, it is marked as a moving object area because it is likely to be a mass with the previous moving object area.
  • determination based on a motion vector is impossible. Accordingly, the intra picture located adjacent to the image block already detected as the moving object region is estimated as a mass together with the previously extracted moving object region.
  • Step S300 The interpolation is applied to the moving object areas detected at S100 and S200 to clean up the fragmentation of the moving object area.
  • the moving object area since it is determined whether the moving object area is the image block unit, even though it is actually a moving object (for example, a person), there is an image block that is not marked as the moving object area in the middle.
  • the phenomenon of dividing into may occur. Accordingly, if there are one or a few unmarked image blocks surrounded by a plurality of image blocks marked with the moving object region, they additionally mark the moving object region. By doing so, it is possible to make the mobile object region divided into several into one. The influence of such interpolation is clearly seen when comparing FIG. 8 and FIG.
  • the moving object region was quickly extracted from each frame image based on the syntax (motion vector, coding type) of the compressed image through steps S100 to S300.
  • the moving object region derived through this process is merely a concept of a mass of images that seem to have something moving in each frame image, and a concept of an object that is uniformly recognized as a frame progresses in a compressed image. There is no.
  • Step S400 Unique ID is managed for the moving object region extracted based on the syntax from the compressed image.
  • the moving object region is derived from each image frame constituting the compressed image.
  • the image content is not determined by analyzing the image content, but merely extracting a chunk of an image that seems to be moving in the image frame. Accordingly, by assigning and managing a unique ID for the mobile object area, attributes as an object are created in the mobile object area. Through this, the area of the moving object can be treated like an object, not just a region, and the movement of a specific object can be interpreted while moving over a series of frame images in the compressed image.
  • Unique ID management of the mobile object area is handled in the following three cases. If a unique ID is assigned in a previous frame and a moving object area is identified in the current frame image (S410), the moving object area identified as an unassigned ID in the current frame image because it has not been identified in the previous frame. In the case of newly assigning a unique ID to the SID, a mobile object region in which a unique ID is assigned in the previous frame but disappeared from the current frame image is identified and revokes the allocated unique ID (S430).
  • the image block is a moving object region without checking the contents of the original image, it is not possible to confirm whether the chunks of the moving object region are actually the same in the image frames before and after. That is, since the contents of the moving object area are not known, such a change cannot be identified, for example, when the cat is replaced by a dog between the front and rear frames at the same point. However, considering that the time interval between frames is very short and that the observation object of the CCTV camera moves at a normal speed, the possibility of this happening can be excluded.
  • the present invention estimates that the ratio or number of image blocks overlapping between the chunks of the moving object region in the front and back frames is equal to or greater than a predetermined threshold. According to this approach, even if the contents of the image are not known, it is possible to determine whether the previously identified moving object region is moved or whether a new moving object region is newly discovered or the existing moving object region is lost. This judgment is lower in accuracy than the prior art, but can greatly increase the data processing speed, which is advantageous in practical applications.
  • the previously allocated Unique ID is allocated to the corresponding moving object region.
  • the identification may be marked in the management database of the Unique ID.
  • step S420 when a new object is unidentified in the current frame image because it has not been identified in the previous frame, a new ID is newly assigned to the corresponding mobile object area. This means that a new moving object is found in the image. 10 illustrates an example in which unique IDs are allocated to three moving object regions derived from CCTV images.
  • Step S500 Next, a thumbnail image and coordinate information (eg, location information and size information) of the moving object area are derived.
  • FIG. 11 is a diagram illustrating an example in which thumbnail images are derived for three moving object areas of a CCTV photographing image in the present invention, and FIG. 12 shows position information and size information as coordinate information for these moving object areas. It is a figure which shows the identified example.
  • the object region identification apparatus 200 may be configured to have a function of decoding a compressed image or selectively decoding a portion of the compressed image. Meanwhile, when the object region identification apparatus 200 transmits the location information to the image analysis system 300, the image analysis system 300 may be configured to obtain a thumbnail image of the moving object region therefrom.
  • the image analysis system 300 since the internal process of the image analysis system 300 has to be changed a lot compared with the prior art, it is determined that it is not a very preferable approach. Rather, a method of generating a thumbnail image of the object region identification apparatus 200 is more preferable.
  • the position information of the moving object region means a position where the moving object region is disposed in the image of the corresponding video block.
  • the upper left coordinate of the rectangle optimally surrounding the moving object region may be used, or the rectangular
  • the center grid can also be used as location information.
  • the size information may use a rectangular size that optimally surrounds the moving object region as shown in FIG. 12.
  • the object region identification apparatus 200 performs unique ID management on the moving object region, and through this, the moving object region in the compressed image is not simply a region but a concept of an object. To have it. Therefore, the image analysis system 300 may treat the series of moving object region identification information provided by the object region identification apparatus 200 in the concept of an object.
  • the image analysis system 300 sorts a plurality of moving object region identification information (thumbnail image, coordinate information) transmitted from the object region identification apparatus 200 based on a unique ID and performs image analysis in units of a unique ID. Through such image analysis, it is possible to recognize the contents of the moving object area by unique ID, and accordingly, the object classification result (eg, person, car, animal, etc.) regarding what the object is, and the object in the image Obtain event identification results (e.g. offender roaming, wall talks, crimes, fights, etc.) as to whether or not you are acting. In this case, the image analysis system 300 does not perform image analysis on the entire compressed image, but performs image analysis only on a series of moving object regions derived by the object region identification apparatus 200 from the compressed image. Significantly lower than the prior art.
  • the object region identification apparatus 200 uses something in the image using syntax information of the compressed image, for example, a motion vector and a coding type.
  • syntax information of the compressed image for example, a motion vector and a coding type.
  • This process is conceptually characterized in that it does not recognize the moving object by interpreting the contents of the image, but extracts a block of the image block that is assumed to contain the moving object without knowing the contents. have.
  • FIG. 5 is a flowchart illustrating an example of a process of detecting effective motion from a compressed image in the present invention
  • FIG. 6 is a diagram illustrating an example of a result of applying the effective motion region detection process according to the present invention to a CCTV compressed image.
  • the process of FIG. 5 corresponds to step S100 in FIG. 4.
  • Step S110 First, a coding unit of a compressed image is parsed to obtain a motion vector and a coding type.
  • a video decoding apparatus performs parsing (header parsing) and motion vector operations on a stream of compressed video according to a video compression standard such as H.264 AVC and H.265 HEVC. Through this process, the motion vector and coding type are parsed for the coding unit of the compressed image.
  • Step S120 Acquire a motion vector cumulative value for a preset time (for example, 500 ms) for each of the plurality of image blocks constituting the compressed image.
  • This step is presented with the intention to detect if there are effective movements that are practically recognizable from the compressed image, such as driving cars, running people, and fighting crowds. Shaky leaves, ghosts that appear momentarily, and shadows that change slightly due to light reflections, though they are moving, are virtually meaningless objects and should not be detected.
  • a motion vector cumulative value is obtained by accumulating a motion vector in units of one or more image blocks for a predetermined time period (for example, 500 msec).
  • the image block is used as a concept including a macroblock and a subblock.
  • Steps S130 and S140 Comparing a motion vector cumulative value with respect to a plurality of image blocks with a preset first threshold value (for example, 20 pixels), and moving the video block having a motion vector cumulative value exceeding the first threshold value. Mark the area.
  • a preset first threshold value for example, 20 pixels
  • an image block having a predetermined motion vector accumulation value is found as described above, it is considered that something significant movement, that is, effective movement, is found in the image block and is marked as a moving object region. For example, we want to detect and detect object movements that are worth the attention of the controller, to the extent that a person runs. On the contrary, even if a motion vector is generated, if the cumulative value for a predetermined time is small enough not to exceed the first threshold, the change in the image is assumed to be small and insignificant and is neglected in the detection step.
  • FIG. 6 is an example illustrating a result of detecting an effective motion region from a CCTV compressed image through the process of FIG. 5.
  • an image block having a motion vector accumulation value equal to or greater than a first threshold is marked as a moving object area and displayed as a bold line area.
  • the sidewalk block, the road, and the part with the shadow are not displayed as the moving object area, while the walking people or the driving car are displayed as the moving object area.
  • FIG. 7 is a flowchart illustrating an example of a process of detecting a boundary region for a moving object region in the present invention
  • FIG. 8 is a boundary region according to FIG. 7 with respect to the CCTV image of FIG. Figure 1 shows an example of the results of further applying the detection process.
  • the process of FIG. 7 corresponds to step S200 in FIG. 4.
  • marking is not properly performed on an object (moving object) actually moving in the image, and only some of them are marked. In other words, if you look at a person walking or driving a car, you will find that not all of the objects are marked, but only some blocks. In addition, although it is actually one moving object, many are marked as if they are a plurality of moving object areas. This means that the criterion of the moving object region adopted in (S100) above was useful for filtering out the general region but was a very strict condition. Therefore, it is necessary to detect the boundary of the moving object by looking around the moving object area.
  • Step S210 First, a plurality of adjacent image blocks are identified based on the image blocks marked as moving object areas by the previous S100. In the present specification, these are referred to as 'neighborhood blocks'. These neighboring blocks are portions that are not marked as the moving object region by S100, and the process of FIG. 7 further examines them to determine whether any of these neighboring blocks may be included in the boundary of the moving object region.
  • Steps S220 and S230 compare a motion vector value with respect to a plurality of neighboring blocks with a second preset threshold (eg, 0), and mark the neighboring block having a motion vector value exceeding the second threshold as a moving object region. do. If the movement is located adjacent to the area of the moving object where effective motion that is practically meaningful is found and a certain amount of movement is found for itself, the image block is likely to be a block with the area of the adjacent moving object due to the characteristics of the photographed image. . Therefore, such neighboring blocks are also marked as moving object regions.
  • a second preset threshold eg, 0
  • Step S240 Also, the coding type is an intra picture among the plurality of neighboring blocks as a moving object region.
  • an intra picture since a motion vector does not exist, it is fundamentally impossible to determine whether a motion exists in a corresponding neighboring block based on the motion vector. In this case, it is safer for the intra picture located adjacent to the image block already detected as the moving object region to maintain the settings of the previously extracted moving object region.
  • FIG. 8 is a diagram visually showing a result of applying a boundary region detection process to a CCTV compressed image.
  • a plurality of image blocks marked as a moving object region through the above process are indicated by a bold line.
  • the area of the moving object was further extended to the vicinity of the moving object area indicated by the bold line in FIG. 6, and thus, it was found that the moving object area was enough to cover the moving object when compared to the image captured by CCTV. Can be.
  • FIG. 9 is a diagram illustrating an example of a result of arranging a moving object region through interpolation according to the present invention for a CCTV image image to which the boundary region detection process illustrated in FIG. 8 is applied.
  • Step S300 is a process of arranging the division of the moving object area by applying interpolation to the moving object areas detected in the previous steps S100 and S200.
  • an unmarked image block is found between the moving object regions indicated by the bold lines. If there is an unmarked image block in the middle, it can be regarded as if they are a plurality of individual moving objects. When the moving object region is fragmented in this way, the result of step S500 may be inaccurate, and the number of moving object regions may increase, thereby complicating the process of steps S500 to S700.
  • the present invention if there is one or a few unmarked image blocks surrounded by a plurality of image blocks marked as the moving object region, this is marked as the moving object region, which is called interpolation.
  • interpolation In contrast to FIG. 8, all of the non-marked image blocks existing between the moving object regions are marked as moving object regions.
  • the moving object region properly reflects the situation of the actual image through the boundary region detection process and the interpolation process.
  • FIG. 6 if the block is marked as a bold line area, a large number of very small objects move in the image screen, which does not correspond to reality.
  • it is determined as a block marked with the bold line area in Fig. 9 will be treated as a few moving objects having a certain volume to reflect the actual scene similarly.
  • the present invention may be embodied in the form of computer readable codes on a computer readable nonvolatile recording medium.
  • Such nonvolatile recording media include various types of storage devices, such as hard disks, SSDs, CD-ROMs, NAS, magnetic tapes, web disks, and cloud disks. Forms that are implemented and executed may also be implemented.
  • the present invention may be implemented in the form of a computer program stored in a medium in combination with hardware to execute a specific procedure.

Landscapes

  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Compression Or Coding Systems Of Tv Signals (AREA)

Abstract

La présente invention concerne une technique pour améliorer les performances de traitement pour une pluralité d'images compressées dans un système de commande de CCTV, et analogues. Plus spécifiquement, la présente invention concerne une technique pour améliorer les performances de traitement d'une image compressée par : l'extraction, par un système d'analyse d'image, d'une zone dans laquelle un mouvement significatif existe dans une image, c'est-à-dire une zone d'objet mobile, sur la base d'une syntaxe (par exemple : un vecteur de mouvement et un type de codage) de l'image compressée sans avoir besoin d'une identification d'objet et d'une reconnaissance de comportement par l'intermédiaire d'un traitement d'image compliqué pour l'ensemble de l'image compressée comme dans la technique classique ; et ensuite l'analyse d'un résultat de l'extraction par interfonctionnement avec le système d'analyse d'image. Selon la présente invention, il est avantageux que les performances de traitement d'une image compressée puissent être considérablement améliorées en identifiant rapidement une zone d'objet mobile sur la base de la syntaxe de l'image compressée sans avoir besoin d'effectuer un traitement d'image compliqué pour l'ensemble de l'image compressée comme dans la technique classique, puis en traitant sélectivement uniquement une partie identifiée par interfonctionnement avec un système d'analyse d'image.
PCT/KR2019/009374 2018-07-30 2019-07-29 Système d'analyse d'image basé sur la syntaxe pour image compressée, et procédé de traitement d'interfonctionnement WO2020027513A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
KR10-2018-0088411 2018-07-30
KR1020180088411A KR102090785B1 (ko) 2018-07-30 2018-07-30 압축영상에 대한 신택스 기반의 영상분석 시스템과 연동 처리 방법

Publications (1)

Publication Number Publication Date
WO2020027513A1 true WO2020027513A1 (fr) 2020-02-06

Family

ID=69231920

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2019/009374 WO2020027513A1 (fr) 2018-07-30 2019-07-29 Système d'analyse d'image basé sur la syntaxe pour image compressée, et procédé de traitement d'interfonctionnement

Country Status (2)

Country Link
KR (1) KR102090785B1 (fr)
WO (1) WO2020027513A1 (fr)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102264252B1 (ko) * 2021-01-18 2021-06-14 보은전자방송통신(주) 압축 영상에서의 이동객체 검출방법 및 이를 수행하는 영상 감시 시스템
KR102343029B1 (ko) * 2021-11-11 2021-12-24 이노뎁 주식회사 모션벡터 기반 분기처리를 이용한 압축영상의 영상분석 처리 방법
KR102459813B1 (ko) * 2022-02-17 2022-10-27 코디오 주식회사 영상스위칭 기반의 주기적 화질보정 영상처리 방법
KR20230165696A (ko) 2022-05-27 2023-12-05 주식회사 다누시스 광학흐름을 통해 객체를 검출하는 시스템
KR20240059841A (ko) 2022-10-28 2024-05-08 (주)한국플랫폼서비스기술 효율적인 동영상 객체 탐지를 위한 트래킹 방법 및 장치

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2000295600A (ja) * 1999-04-08 2000-10-20 Toshiba Corp 監視装置
JP2002262296A (ja) * 2001-02-28 2002-09-13 Mitsubishi Electric Corp 移動物体検出装置、および画像監視システム
KR100883632B1 (ko) * 2008-08-13 2009-02-12 주식회사 일리시스 고해상도 카메라를 이용한 지능형 영상 감시 시스템 및 그 방법
KR20100010734A (ko) * 2008-07-23 2010-02-02 한국철도기술연구원 스테레오 카메라 및 열화상 카메라를 이용한 승강장모니터링 시스템 및 그 방법
US9215467B2 (en) * 2008-11-17 2015-12-15 Checkvideo Llc Analytics-modulated coding of surveillance video

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2000295600A (ja) * 1999-04-08 2000-10-20 Toshiba Corp 監視装置
JP2002262296A (ja) * 2001-02-28 2002-09-13 Mitsubishi Electric Corp 移動物体検出装置、および画像監視システム
KR20100010734A (ko) * 2008-07-23 2010-02-02 한국철도기술연구원 스테레오 카메라 및 열화상 카메라를 이용한 승강장모니터링 시스템 및 그 방법
KR100883632B1 (ko) * 2008-08-13 2009-02-12 주식회사 일리시스 고해상도 카메라를 이용한 지능형 영상 감시 시스템 및 그 방법
US9215467B2 (en) * 2008-11-17 2015-12-15 Checkvideo Llc Analytics-modulated coding of surveillance video

Also Published As

Publication number Publication date
KR20200013340A (ko) 2020-02-07
KR102090785B1 (ko) 2020-03-18

Similar Documents

Publication Publication Date Title
WO2020027513A1 (fr) Système d'analyse d'image basé sur la syntaxe pour image compressée, et procédé de traitement d'interfonctionnement
WO2019124635A1 (fr) Procédé orienté syntaxe de détection d'une intrusion d'objet dans une vidéo comprimée
KR102187376B1 (ko) 딥러닝 이미지 분석과 연동하는 신택스 기반의 선별 관제 제공 방법
WO2018030658A1 (fr) Procédé de détection d'un objet en mouvement à partir d'une image cctv stockée, via un traitement de reconstruction d'image
WO2020171388A2 (fr) Procédé d'identification d'un objet ayant un mouvement anormal dans une image compressée à l'aide d'une trajectoire et d'un motif de vecteur de mouvement
WO2020027512A1 (fr) Procédé de commande de suivi d'objet basé sur syntaxe destiné à une image comprimée par un appareil photo ptz
WO2019039661A1 (fr) Procédé d'extraction basée sur la syntaxe d'une région d'objet mobile d'une vidéo compressée
WO2012137994A1 (fr) Dispositif de reconnaissance d'image et son procédé de surveillance d'image
EP3405889A1 (fr) Plateforme dématérialisée à synchronisation de caméras multiples
US9256789B2 (en) Estimating motion of an event captured using a digital video camera
WO2020027511A1 (fr) Procédé de génération d'une carte thermique basée sur la syntaxe pour une image compressée
WO2016064107A1 (fr) Procédé et appareil de lecture vidéo sur la base d'une caméra à fonctions de panoramique/d'inclinaison/de zoom
WO2019124634A1 (fr) Procédé orienté syntaxe de suivi d'objet dans une vidéo comprimée
KR102061915B1 (ko) 압축영상에 대한 신택스 기반의 객체 분류 방법
WO2019124636A1 (fr) Procédé basé sur la syntaxe pour détecter une circulation routière à contresens dans une vidéo compressée
WO2019124632A1 (fr) Procédé orienté syntaxe de détection d'objet de flânerie dans une vidéo comprimée
WO2019124633A1 (fr) Procédé orienté syntaxe de détection d'objet d'escalade de mur dans une vidéo comprimée
KR102179077B1 (ko) 상용분류기 외부 연동 학습형 신경망을 이용한 압축영상에 대한 신택스 기반의 객체 분류 방법
KR102178952B1 (ko) 압축영상에 대한 신택스 기반의 mrpn-cnn을 이용한 객체 분류 방법
WO2022231053A1 (fr) Procédé et appareil de traitement d'image multi-résolution en mesure de reconnaître une pluralité d'objets dynamiques
WO2011043499A1 (fr) Procédé de reconnaissance de modification de contexte et dispositif de traitement d'images utilisant un tel procédé
CN109886234B (zh) 目标检测方法、装置、系统、电子设备、存储介质
KR102153093B1 (ko) 컨텍스트를 고려한 압축영상에 대한 신택스 기반의 이동객체 영역 추출 방법
JP4736171B2 (ja) 監視用信号の通信方法、及び監視用信号通信装置
KR102343029B1 (ko) 모션벡터 기반 분기처리를 이용한 압축영상의 영상분석 처리 방법

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19844244

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19844244

Country of ref document: EP

Kind code of ref document: A1